import cv2
import numpy as np
import torch


def mask_score(mask):
    '''Scoring the mask according to connectivity.'''
    mask = mask.astype(np.uint8)
    if mask.sum() < 10:
        return 0
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL,
                                   cv2.CHAIN_APPROX_NONE)
    cnt_area = [cv2.contourArea(cnt) for cnt in contours]
    conc_score = np.max(cnt_area) / sum(cnt_area)
    return conc_score


def sobel(img, mask, thresh=50):
    '''Calculating the high-frequency map.'''
    H, W = img.shape[0], img.shape[1]
    img = cv2.resize(img, (256, 256))
    mask = (cv2.resize(mask, (256, 256)) > 0.5).astype(np.uint8)
    kernel = np.ones((5, 5), np.uint8)
    mask = cv2.erode(mask, kernel, iterations=2)

    Ksize = 3
    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=Ksize)
    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=Ksize)
    sobel_X = cv2.convertScaleAbs(sobelx)
    sobel_Y = cv2.convertScaleAbs(sobely)
    scharr = cv2.addWeighted(sobel_X, 0.5, sobel_Y, 0.5, 0)
    scharr = np.max(scharr, -1) * mask

    scharr[scharr < thresh] = 0.0
    scharr = np.stack([scharr, scharr, scharr], -1)
    scharr = (scharr.astype(np.float32) / 255 * img.astype(np.float32)).astype(
        np.uint8)
    scharr = cv2.resize(scharr, (W, H))
    return scharr


def resize_and_pad(image, box):
    '''Fitting an image to the box region while keeping the aspect ratio.'''
    y1, y2, x1, x2 = box
    H, W = y2 - y1, x2 - x1
    h, w = image.shape[0], image.shape[1]
    r_box = W / H
    r_image = w / h
    if r_box >= r_image:
        h_target = H
        w_target = int(w * H / h)
        image = cv2.resize(image, (w_target, h_target))

        w1 = (W - w_target) // 2
        w2 = W - w_target - w1
        pad_param = ((0, 0), (w1, w2), (0, 0))
        image = np.pad(image, pad_param, 'constant', constant_values=255)
    else:
        w_target = W
        h_target = int(h * W / w)
        image = cv2.resize(image, (w_target, h_target))

        h1 = (H - h_target) // 2
        h2 = H - h_target - h1
        pad_param = ((h1, h2), (0, 0), (0, 0))
        image = np.pad(image, pad_param, 'constant', constant_values=255)
    return image


def expand_image_mask(image, mask, ratio=1.4):
    h, w = image.shape[0], image.shape[1]
    H, W = int(h * ratio), int(w * ratio)
    h1 = int((H - h) // 2)
    h2 = H - h - h1
    w1 = int((W - w) // 2)
    w2 = W - w - w1

    pad_param_image = ((h1, h2), (w1, w2), (0, 0))
    pad_param_mask = ((h1, h2), (w1, w2))
    image = np.pad(image, pad_param_image, 'constant', constant_values=255)
    mask = np.pad(mask, pad_param_mask, 'constant', constant_values=0)
    return image, mask


def resize_box(yyxx, H, W, h, w):
    y1, y2, x1, x2 = yyxx
    y1, y2 = int(y1 / H * h), int(y2 / H * h)
    x1, x2 = int(x1 / W * w), int(x2 / W * w)
    y1, y2 = min(y1, h), min(y2, h)
    x1, x2 = min(x1, w), min(x2, w)
    return (y1, y2, x1, x2)


def get_bbox_from_mask(mask):
    h, w = mask.shape[0], mask.shape[1]

    if mask.sum() < 10:
        return 0, h, 0, w
    rows = np.any(mask, axis=1)
    cols = np.any(mask, axis=0)
    y1, y2 = np.where(rows)[0][[0, -1]]
    x1, x2 = np.where(cols)[0][[0, -1]]
    return (y1, y2, x1, x2)


def expand_bbox(mask, yyxx, ratio=[1.2, 2.0], min_crop=0):
    y1, y2, x1, x2 = yyxx
    ratio = np.random.randint(ratio[0] * 10, ratio[1] * 10) / 10
    H, W = mask.shape[0], mask.shape[1]
    xc, yc = 0.5 * (x1 + x2), 0.5 * (y1 + y2)
    h = ratio * (y2 - y1 + 1)
    w = ratio * (x2 - x1 + 1)
    h = max(h, min_crop)
    w = max(w, min_crop)

    x1 = int(xc - w * 0.5)
    x2 = int(xc + w * 0.5)
    y1 = int(yc - h * 0.5)
    y2 = int(yc + h * 0.5)

    x1 = max(0, x1)
    x2 = min(W, x2)
    y1 = max(0, y1)
    y2 = min(H, y2)
    return (y1, y2, x1, x2)


def box2squre(image, box):
    H, W = image.shape[0], image.shape[1]
    y1, y2, x1, x2 = box
    cx = (x1 + x2) // 2
    cy = (y1 + y2) // 2
    h, w = y2 - y1, x2 - x1

    if h >= w:
        x1 = cx - h // 2
        x2 = cx + h // 2
    else:
        y1 = cy - w // 2
        y2 = cy + w // 2
    x1 = max(0, x1)
    x2 = min(W, x2)
    y1 = max(0, y1)
    y2 = min(H, y2)
    return (y1, y2, x1, x2)


def pad_to_square(image, pad_value=255, random=False):
    H, W = image.shape[0], image.shape[1]
    if H == W:
        return image

    padd = abs(H - W)
    if random:
        padd_1 = int(np.random.randint(0, padd))
    else:
        padd_1 = int(padd / 2)
    padd_2 = padd - padd_1

    if H > W:
        pad_param = ((0, 0), (padd_1, padd_2), (0, 0))
    else:
        pad_param = ((padd_1, padd_2), (0, 0), (0, 0))

    image = np.pad(image, pad_param, 'constant', constant_values=pad_value)
    return image


def box_in_box(small_box, big_box):
    y1, y2, x1, x2 = small_box
    y1_b, _, x1_b, _ = big_box
    y1, y2, x1, x2 = y1 - y1_b, y2 - y1_b, x1 - x1_b, x2 - x1_b
    return (y1, y2, x1, x2)


def shuffle_image(image, N):
    height, width = image.shape[:2]

    block_height = height // N
    block_width = width // N
    blocks = []

    for i in range(N):
        for j in range(N):
            block = image[i * block_height:(i + 1) * block_height,
                          j * block_width:(j + 1) * block_width]
            blocks.append(block)

    np.random.shuffle(blocks)
    shuffled_image = np.zeros((height, width, 3), dtype=np.uint8)

    for i in range(N):
        for j in range(N):
            shuffled_image[i * block_height:(i + 1) * block_height,
                           j * block_width:(j + 1)
                           * block_width] = blocks[i * N + j]
    return shuffled_image


def get_mosaic_mask(image, fg_mask, N=16, ratio=0.5):
    ids = [i for i in range(N * N)]
    masked_number = int(N * N * ratio)
    masked_id = np.random.choice(ids, masked_number, replace=False)

    height, width = image.shape[:2]
    mask = np.ones((height, width))

    block_height = height // N
    block_width = width // N

    b_id = 0
    for i in range(N):
        for j in range(N):
            if b_id in masked_id:
                mask[i * block_height:(i + 1) * block_height,
                     j * block_width:(j + 1)
                     * block_width] = mask[i * block_height:(i + 1)
                                           * block_height, j * block_width:
                                           (j + 1) * block_width] * 0
            b_id += 1
    mask = mask * fg_mask
    mask3 = np.stack([mask, mask, mask], -1).copy().astype(np.uint8)
    noise = q_x(image)
    noise_mask = image * mask3 + noise * (1 - mask3)
    return noise_mask


def extract_canny_noise(image, mask, dilate=True):
    h, w = image.shape[0], image.shape[1]
    mask = cv2.resize(mask.astype(np.uint8), (w, h)) > 0.5
    kernel = np.ones((8, 8), dtype=np.uint8)
    mask = cv2.erode(mask.astype(np.uint8), kernel, 10)

    canny = cv2.Canny(image, 50, 100) * mask
    kernel = np.ones((8, 8), dtype=np.uint8)
    mask = (cv2.dilate(canny, kernel, 5) > 128).astype(np.uint8)
    mask = np.stack([mask, mask, mask], -1)

    pure_noise = q_x(image, t=1) * 0 + 255
    canny_noise = mask * image + (1 - mask) * pure_noise
    return canny_noise


def get_random_structure(size):
    choice = np.random.randint(1, 5)

    if choice == 1:
        return cv2.getStructuringElement(cv2.MORPH_RECT, (size, size))
    elif choice == 2:
        return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size, size))
    elif choice == 3:
        return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size, size // 2))
    elif choice == 4:
        return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size // 2, size))


def random_dilate(seg, min=3, max=10):
    size = np.random.randint(min, max)
    kernel = get_random_structure(size)
    seg = cv2.dilate(seg, kernel, iterations=1)
    return seg


def random_erode(seg, min=3, max=10):
    size = np.random.randint(min, max)
    kernel = get_random_structure(size)
    seg = cv2.erode(seg, kernel, iterations=1)
    return seg


def compute_iou(seg, gt):
    intersection = seg * gt
    union = seg + gt
    return (np.count_nonzero(intersection) + 1e-6) / (
        np.count_nonzero(union) + 1e-6)


def select_max_region(mask):
    nums, labels, stats, centroids = cv2.connectedComponentsWithStats(
        mask, connectivity=8)
    background = 0
    for row in range(stats.shape[0]):
        if stats[row, :][0] == 0 and stats[row, :][1] == 0:
            background = row
    stats_no_bg = np.delete(stats, background, axis=0)
    max_idx = stats_no_bg[:, 4].argmax()
    max_region = np.where(labels == max_idx + 1, 1, 0)

    return max_region.astype(np.uint8)


def perturb_mask(gt, min_iou=0.3, max_iou=0.99):
    iou_target = np.random.uniform(min_iou, max_iou)
    h, w = gt.shape
    gt = gt.astype(np.uint8)
    seg = gt.copy()

    # Rare case
    if h <= 2 or w <= 2:
        print('GT too small, returning original')
        return seg

    # Do a bunch of random operations
    for _ in range(250):
        for _ in range(4):
            lx, ly = np.random.randint(w), np.random.randint(h)
            lw, lh = np.random.randint(lx + 1, w + 1), np.random.randint(
                ly + 1, h + 1)

            # Randomly set one pixel to 1/0. With the following dilate/erode, we can create holes/external regions
            if np.random.rand() < 0.1:
                cx = int((lx + lw) / 2)
                cy = int((ly + lh) / 2)
                seg[cy, cx] = np.random.randint(2) * 255

            # Dilate/erode
            if np.random.rand() < 0.5:
                seg[ly:lh, lx:lw] = random_dilate(seg[ly:lh, lx:lw])
            else:
                seg[ly:lh, lx:lw] = random_erode(seg[ly:lh, lx:lw])

            seg = np.logical_or(seg, gt).astype(np.uint8)
            # seg = select_max_region(seg)

        if compute_iou(seg, gt) < iou_target:
            break
    seg = select_max_region(seg.astype(np.uint8))
    return seg.astype(np.uint8)


def q_x(x_0, t=65):
    '''Adding noise for and given image.'''
    x_0 = torch.from_numpy(x_0).float() / 127.5 - 1
    num_steps = 100

    betas = torch.linspace(-6, 6, num_steps)
    betas = torch.sigmoid(betas) * (0.5e-2 - 1e-5) + 1e-5

    alphas = 1 - betas
    alphas_prod = torch.cumprod(alphas, 0)

    alphas_bar_sqrt = torch.sqrt(alphas_prod)
    one_minus_alphas_bar_sqrt = torch.sqrt(1 - alphas_prod)

    noise = torch.randn_like(x_0)
    alphas_t = alphas_bar_sqrt[t]
    alphas_1_m_t = one_minus_alphas_bar_sqrt[t]
    return (alphas_t * x_0 + alphas_1_m_t * noise).numpy() * 127.5 + 127.5


def extract_target_boundary(img, target_mask):
    Ksize = 3
    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=Ksize)
    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=Ksize)

    # sobel-x
    sobel_X = cv2.convertScaleAbs(sobelx)
    # sobel-y
    sobel_Y = cv2.convertScaleAbs(sobely)
    # sobel-xy
    scharr = cv2.addWeighted(sobel_X, 0.5, sobel_Y, 0.5, 0)
    scharr = np.max(scharr, -1).astype(np.float32) / 255
    scharr = scharr * target_mask.astype(np.float32)
    return scharr